Magnetic Resonance Imaging (MRI) has been widely used for non-invasive diagnosis due to its high resolution and excellent soft tissue contrast. However, its long acquisition time is a drawback and predisposes to artifacts due to patient movement. Undersampling is an efficient way to shorten the acquisition time but the undersampling process itself may in turn produce undersampling artifacts and degrade image quality. In the past decades, methods such as compressed sensing (CS) use sparsity constraints to reconstruct images [1]}, [2]}, [3]} but CS typically suffers from long computational time. The recent development of deep learning combined with CS acquisition patterns enabled fast acquisition and fast reconstruction and achieved state-of-the-art performance on Cartesian trajectories for MRI [4]}, [5]}, [6]}.
